Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update
|dc.contributor.author||Vo, Ba Tuong|
|dc.identifier.citation||Jones, B. and Vo, B.T. and Vo, B. 2016. Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update, in Proceedings of the Astrodynamics Specialist Conference (AIAA/AAS), Sep 13-16 2016, pp. 1177-1194. Long Beach, California: American Institute of Aeronautics and Astronautics.|
Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and leverages a joint prediction and update with Gibbs sampling to improve computational efficiency. Based on the use of labeled random finite sets, the d-Generalized Labeled Multi-Bernoulli Filter provides a closed-form solution to the Bayes recursion for a multi-target filter. A similar filter, the Labeled Multi-Bernoulli Filter, is a principled approximation to reduce computational complexity. Upon combining these filters with astrodynamics-based models for orbit state probability density function prediction and initial orbit determination, a 100-object simulation is used to demonstrate the ability of these tools to track space objects in near-geosynchronous orbit. Both filters converge on solutions with sub-500 meter accuracy and demonstrate similar performance as a function of detection probability, clutter, and the birth model employed. A robust comparison of the two filters requires further Monte Carlo-based tests to quantify variance in the solutions due to random inputs.
|dc.title||Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update|
|dcterms.source.title||AIAA/AAS Astrodynamics Specialist Conference, 2016|
|dcterms.source.series||AIAA/AAS Astrodynamics Specialist Conference, 2016|
|curtin.department||Department of Electrical and Computer Engineering|
|curtin.accessStatus||Fulltext not available|
Files in this item
There are no files associated with this item.